ion energies and further decompose via three-body fragmentation processes. Experiments with d1-PA (CH3COCOOD) support the interpretations. The dissociation on S3 is fast, as indicated by the products' recoil angular anisotropy, but the roles of internal conversion and intersystem crossing to lower states are yet to be determined.Machine learning force field (ML-FF) has emerged as a potential promising approach to simulate various material phenomena for large systems with ab initio accuracy. However, most ML-FFs have been used to study the phenomena relatively close to the equilibrium ground states. In this work, we have studied a far from equilibrium system of liquid to crystal Si growth using ML-FF. We found that our ML-FF based on ab initio decomposed atomic energy can reproduce all the aspects of ab initio simulated growth, from local energy fluctuations to transition temperatures, to diffusion constant, and growth rates. We have also compared the growth simulation with the Stillinger-Weber classical force field and found significant differences. A procedure is also provided to correct a systematic fitting bias in the ML-FF training process, which exists in all training models, otherwise critical results like transition temperature will be wrong.Monolayer transition metal dichalcogenide semiconductors, with versatile experimentally accessible exciton species, offer an interesting platform for investigating the interaction between excitons and a Fermi sea of charges. https://www.selleckchem.com/products/itacnosertib.html Using hexagonal boron nitride encapsulated monolayer MoSe2, we study the impact of charge density tuning on the A and B series of exciton Rydberg states, including A1s, A2s, B1s, and B2s. The doping dependence of the A2s state provides an opportunity to examine such interactions with greatly reduced exciton binding energy and more spatially diffuse structures, and we found that the impact of the Fermi sea becomes much more dramatic compared to the A1s state. Using photoluminescence upconversion, we verify that the B2s exciton state displays similar behavior when interacting with the Fermi sea despite being well above the bare bandgap in energy. Photoluminescence and reflection spectra of the A1s state show clear evidence that the interaction of the exciton with a Fermi sea is best described by the exciton-polaron model, rather than a trion model. Our experimental results demonstrate that overall features of charge interaction are quite generic and highly robust, offering key insights into the dressed many body states in a Fermi sea.We present a scale-bridging approach based on a multi-fidelity (MF) machine-learning (ML) framework leveraging Gaussian processes (GP) to fuse atomistic computational model predictions across multiple levels of fidelity. Through the posterior variance of the MFGP, our framework naturally enables uncertainty quantification, providing estimates of confidence in the predictions. We used density functional theory as high-fidelity prediction, while a ML interatomic potential is used as low-fidelity prediction. Practical materials' design efficiency is demonstrated by reproducing the ternary composition dependence of a quantity of interest (bulk modulus) across the full aluminum-niobium-titanium ternary random alloy composition space. The MFGP is then coupled to a Bayesian optimization procedure, and the computational efficiency of this approach is demonstrated by performing an on-the-fly search for the global optimum of bulk modulus in the ternary composition space. The framework presented in this manuscript is the first application of MFGP to atomistic materials simulations fusing predictions between density functional theory and classical interatomic potential calculations.The ability of the F atom of HC≡CF, H2C=CHF and H3CCH2F to serve as an electron donor to the triel (Tr) atom of TrR3 in the context of a triel bond is assessed by ab initio calculations. The triel bond formed by Csp3-F is strongest, as high as 30 kcal/mol, followed by Csp2-F, and then by Csp-F whose triel bonds can be as small as 1 kcal/mol. The noncovalent bond strength diminishes in the order Tr = Al &gt; Ga &gt; B, consistent with the intensity of the π-hole above the Tr atom in the monomer. The triel bond strength of the Al and Ga complexes increases along with the electronegativity of the R substituent but is largest for R=H when Tr=B. Electrostatics play the largest role in the stronger triel bonds, but dispersion makes an outsized contribution for the weakest such bonds.We study the dependence of kinetic energy densities (KEDs) on density-dependent variables that have been suggested in previous works on kinetic energy functionals for orbital-free density functional theory. We focus on the role of data distribution and on data and regressor selection. We compare unweighted and weighted linear and Gaussian process regressions of KEDs for light metals and a semiconductor. We find that good quality linear regression resulting in good energy-volume dependence is possible over density-dependent variables suggested in previous literature studies. This is achieved with weighted fitting based on the KED histogram. With Gaussian process regressions, excellent KED fit quality well exceeding that of linear regressions is obtained as well as a good energy-volume dependence, which was somewhat better than that of best linear regressions. We find that while the use of the effective potential as a descriptor improves linear KED fitting, it does not improve the quality of the energy-volume dependence with linear regressions but substantially improves it with Gaussian process regression. Gaussian process regression is also able to perform well without data weighting.This is our current research perspective on models providing insight into statistical mechanics. It is necessarily personal, emphasizing our own interest in simulation as it developed from the National Laboratories' work to the worldwide explosion of computation of today. We contrast the past and present in atomistic simulations, emphasizing those simple models that best achieve reproducibility and promote understanding. Few-body models with pair forces have led to today's "realistic" simulations with billions of atoms and molecules. Rapid advances in computer technology have led to change. Theoretical formalisms have largely been replaced by simulations incorporating ingenious algorithm development. We choose to study particularly simple, yet relevant, models directed toward understanding general principles. Simplicity remains a worthy goal, as does relevance. We discuss hard-particle virial series, melting, thermostatted oscillators with and without heat conduction, chaotic dynamics, fractals, the connection of Lyapunov spectra to thermodynamics, and finally simple linear maps.